{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Import packages（导入包）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Import third-party packages（导入第三方包）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pygame 2.1.2 (SDL 2.0.18, Python 3.10.6)\n",
      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "#import keras\n",
    "import time\n",
    "import math\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import tensorflow.keras.backend as Kbackend\n",
    "\n",
    "# pydot_ng 用于绘制网络图\n",
    "import pydot_ng as pg\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "# from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from pandas import set_option\n",
    "# from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "from pylab import *\n",
    "from scipy import interpolate\n",
    "from pygame import mixer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# calculate RMSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average', squared=True)\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "numpy+mkl 版本为17.2  以上\n",
    "tensorboard，tensorflow 版本为2.4.0\n",
    "pydot版本为1.4.1, graphviz 版本为0.13.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Import our own package（导入自己的包）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import senutil as sen\n",
    "# from rbflayer import RBFLayer, InitCentersRandom\n",
    "import senmodels as sms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "set_option(\"display.max_rows\", 15)\n",
    "set_option('display.width', 200)\n",
    "np.set_printoptions(suppress=True, threshold=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 使用GPU训练时候，打开下面注释\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='-1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Dataset preparation work（数据集准备工作）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Define the location of the dataset（定义数据集所在位置）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Location of training set（训练集所在位置）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "layer_name = 'QT'   # QSK | QT"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "data/exp_curve_reconstract/exp_1/train/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "TrainDataPath = '../data/exp_curve_reconstract/exp_2/train'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "filename_AB:  \n",
    "(1) GY1_R_0.1250m_QSK_2107m-2587m.csv;\n",
    "(2) GY1_R_ 0.1250m_QT_2587m-2744m.csv;\n",
    "(3)GY1_R_0.1250m_QSK_2107m-2587m_for_C21井_R_ 0.1250m_QSK_1260m-1668m-修正深度_add_sample_weight.csv;\n",
    "(4)GY1_R_0.1250m_QSK_2107m-2587m_for_SYY1_R_0.1250m_QSK_2105m-2448m-修正深度_add_sample_weight.csv;\n",
    "(5)GY1_R_ 0.1250m_QT_2587m-2744m_for_C21井_R_ 0.1250m_QT_1668m-1859m_add_sample_weight.csv;\n",
    "(6)GY1_R_ 0.1250m_QT_2587m-2744m_for_SYY1_R_ 0.1250m_QT_2448m-2529m_add_sample_weight.csv;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/exp_curve_reconstract/exp_2/train\\泉头组\\古页1_R_ 0.1250m_泉头组_2587m-2744m_针对_松页油1_R_ 0.1250m_泉头组_2448m-2529m_add_sample_weight.csv\n"
     ]
    }
   ],
   "source": [
    "filename_AB = 'GY1_R_ 0.1250m_QT_2587m-2744m_for_SYY1_R_ 0.1250m_QT_2448m-2529m_add_sample_weight.csv'   # QSK；  QT\n",
    "TrainDataPath = os.path.join(TrainDataPath,layer_name,filename_AB)\n",
    "print(TrainDataPath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Location of the testing set during the prediction phase(预测阶段测试集所在位置)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "TestDataPath = 'data/exp_curve_reconstract/exp_2/test/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "data/exp_curve_reconstract/exp_1/test/;   data/exp_curve_reconstract/val/"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "filename_A为预测曲线对应的常规曲线数据:  \n",
    "(1) SYY1_R_ 0.1250m_QSK_2105m-2448m-修正深度.csv\n",
    "(2) C21井_R_ 0.1250m_QSK_1260m-1668m-修正深度.csv\n",
    "(3) C21井_R_ 0.1250m_QT_1668m-1859m.csv\n",
    "(4) SYY1_R_ 0.1250m_QT_2448m-2529m.csv\n",
    "QP1井_R_ 0.1000m_QSK_1649m-2089m.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "filename_A =  'SYY1_R_ 0.1250m_QT_2448m-2529m.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "addR_well_name = filename_A.split(\".\")[0]\n",
    "TestDataPath = os.path.join(TestDataPath,layer_name,filename_A)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Location of the real label dataset(真实数据集所在位置)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "用于验证预测结果，实际中可能没有"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "use_high_R_data = True #True # False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# resolution, default value = 10 (对应于0.1m); 8 (对应于0.125m)\n",
    "resolution = 8"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "data/exp_curve_reconstract/exp_1/test/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "HighRDataPath = 'data/exp_curve_reconstract/exp_2/test/'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "理论上与filename_A在同一处，标签文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# C21井_R_ 0.1250m_QSK_1260m-1668m-修正深度.csv\n",
    "# SYY1_R_0.1250m_QSK_2105m-2448m-修正深度.csv\n",
    "\n",
    "# filename_C_H = 'C21井_R_ 0.1250m_QT_1668m-1859m.csv'\n",
    "filename_C_H = filename_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "HighRDataPath = os.path.join(HighRDataPath,layer_name,filename_C_H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Model Definition（模型定义）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Define independent variables（定义自变量）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "定义要输入的维度AC、CNL、DEN、GR、RD、RS等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "input_vectors = [\"DT\",\"CNL\",\"DEN\",\"GR\",\"RD\",\"RS\"]\n",
    "# input_vectors = [\"AC\",\"CNL\",\"DEN\",\"GR\",\"RLLD\",\"RLLS\"]\n",
    "# input_vectors = [\"AC\",\"DEN\",\"GR\",\"RD\",\"RS\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Define dependent variable（定义因变量）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Define the parameter model to be trained, such as DTXX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 定义要目标曲线PERM、POR、SW\n",
    "# element = [\"孔隙度\",\"饱和度\",\"渗透率\"]\n",
    "element = [\"DTXX\"]\n",
    "# 公式计算的物性参数曲线\n",
    "# reference = [\"POR\",\"SW\",\"PERM\"]\n",
    "reference = [\"DTXX\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# element_name = \"孔隙度\"\n",
    "element_name = \"DTXX\"\n",
    "# reference_name = \"POR\"\n",
    "reference_name = \"DTXX\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 样本权重\n",
    "weight_coloum = \"sample_weight\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Select the model to be used（选择要使用的模型）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "择要使用的模型类型model_type:  \n",
    "(1)'RBF'(flag = 1);   \n",
    "(2)'DNN'(flag = 2);  \n",
    "(3)'LSTM','GRU','GRU2','DNN_2'(flag = 3),Capsule;  \n",
    "(4)'BLSTM', 'BGRU'(flag = 3),'MyWaveNet','BLSTM-Atten','BiLSTM-Atten2'，'BiLSTM-Atten3'，'BiLSTM-Atten4','BiLSTM-Atten5','BiLSTM-Atten6','BiGRU-Atten','BiGRU-Atten2'; 目前最好几种   \n",
    "(5)'WaveNet','MyUNet' ,CNN_Atten   \n",
    "(6) (暂未完成)  'NAS','IndyGRU','LSTM-GRU','IndyLSTM','UGRNNCell';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    " # BLSTM ； MyWaveNet; BLSTM-Atten\n",
    "# model_type = 'MyWaveNet' \n",
    "# model_type = 'BiLSTM-Atten5'\n",
    "# model_type = 'CNN_Atten'   Double_Expert_Net\n",
    "model_type = 'Double_Expert_Net'  # (1) MyWaveNet;  (4)BiLSTM-Atten5; (6) BiGRU-Atten2;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Set and model related parameters(设置和模型相关的参数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "flag = 0\n",
    "\n",
    "if model_type == \"RBF\":\n",
    "    flag = 1\n",
    "elif model_type == \"DNN\":\n",
    "    flag = 2\n",
    "else:\n",
    "    flag = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Initialize the training model structure parameters(初始化训练模型结构参数)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "网络结构参数来自与senmodels模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# MAX_SAMPLE_NUM = 2000\n",
    "# 输入维度\n",
    "data_dim = len(input_vectors)\n",
    "seq_length = 8  # 序列长度数 default 10,  TT1:4  J404:8\n",
    "hidden_dim = 12  # 隐藏层神经元数 default 36  \n",
    "# 输出维度\n",
    "output_dim = 1\n",
    "n_layers = 3 # LSTM layer 层数 default 4  \n",
    "dropout_rate = 0.4   # 在训练过程中设置 default 0.2  ,Na,0.4\n",
    "\n",
    "# 修改下面的参数不改变网络\n",
    "learning_rate = 0.005  # default 0.01, 优选：0.005 ；0.008 可用0.0008 0.0005 0.001\n",
    "# batch_size = 100 Na:\n",
    "BATCH_SIZE = 640\n",
    "# iterations = 300\n",
    "EPOCHS = 30\n",
    "\n",
    "# input_vectors_dim = len(input_vectors)\n",
    "\n",
    "# 模型是否归一化\n",
    "operate_standard = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_para = sms.MyModelParameter(data_dim,seq_length, hidden_dim, output_dim,learning_rate,dropout_rate,n_layers,BATCH_SIZE,EPOCHS)\n",
    "\n",
    "model_para.n_layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Is the setting a training operation or a testing operation(设定是训练操作还是测试操作)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "模型有两种阶段：  \"train\"(训练) | \"test\"(测试)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model_stage = \"train\"\n",
    "model_stage = \"test\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "训练模型是否使用权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_use_weight = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "松页油1 古页1\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"train\":\n",
    "    well_name = filename_AB.split(\"_R_\")[0]\n",
    "    train_well_name = filename_AB.split(\"_R_\")[0]\n",
    "    test_well_name = filename_A.split(\"_R_\")[0]\n",
    "else:\n",
    "    well_name = filename_A.split(\"_R_\")[0]\n",
    "    train_well_name = filename_AB.split(\"_R_\")[0]\n",
    "    test_well_name = filename_A.split(\"_R_\")[0]\n",
    "print(well_name,train_well_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 训练完成是否播放音乐 True | False\n",
    "paly_music = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 是否保存训练日志：default : False | True， 保存日志可以用tensorboard显示实时计算日志，但是日志文件占用空间\n",
    "save_logs = False #True\n",
    "# 训练日志保存位置\n",
    "log_path = os.path.join(\"curve_reconstract_logs/\")\n",
    "\n",
    "if os.path.exists(log_path):\n",
    "    pass\n",
    "else:\n",
    "    os.makedirs(log_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 是否使用batch_size_strategy default False , | True\n",
    "batch_size_strategy = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 两种学习率适应方法:  default = 0\n",
    "#(1)每隔10个epoch，学习率减小为原来的1/10, set value = 1;\n",
    "#(2)当学习停滞时，减少2倍或10倍的学习率常常能获得较好的效果。value = 2\n",
    "learning_rate_deacy_policy = 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# use_semi_seqlength = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 是否绘制模型图 default = False；  Value：True | False\n",
    "plot_modelnet = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Location for saving the model during the training phase(训练阶段模型保存位置)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n"
     ]
    }
   ],
   "source": [
    "print(model_stage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "Date = sen.tid_date()\n",
    "child_dir_name = train_well_name + '_Seq_'+ str(seq_length)+ \"_\" + Date + \"/\"\n",
    "custom_model_child_dir = \"J404_Seq_8_WaveNet/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curve_reconstract_model/泉头组_sampleweight_double_expert_net_train/\n"
     ]
    }
   ],
   "source": [
    "if train_use_weight == False:\n",
    "    # 设置模型保存的文件夹\n",
    "    model_save_path = os.path.join(\"curve_reconstract_model/\", layer_name + '_nosampleweight_' + model_type.lower() + \"_train/\")\n",
    "else:\n",
    "    model_save_path = os.path.join(\"curve_reconstract_model/\", layer_name +  '_sampleweight_' + model_type.lower() + \"_train/\")\n",
    "#model_save_path = os.path.join(\"model/\", 'element_' + model_type.lower() + \"_train/\",child_dir_name)\n",
    "if os.path.exists(model_save_path):\n",
    "    model_path = model_save_path\n",
    "else:\n",
    "    os.makedirs(model_save_path)\n",
    "    model_path = model_save_path\n",
    "print(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_name: 古页1_for_松页油1_泉头组_double_expert_net_DTXX_3_layers__lr_0.005h_dim12_epoch_30.h5\n",
      "model_file: curve_reconstract_model/泉头组_sampleweight_double_expert_net_train/古页1_for_松页油1_泉头组_double_expert_net_DTXX_3_layers__lr_0.005h_dim12_epoch_30.h5\n"
     ]
    }
   ],
   "source": [
    "model_name = train_well_name + \"_for_\" + test_well_name + \"_\" + layer_name + \"_\" + model_type.lower() + \"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "        learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS) + \".h5\"\n",
    "model_file = model_path + model_name\n",
    "print(\"model_name:\",model_name)\n",
    "print(\"model_file:\",model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curve_reconstract_model/泉头组_sampleweight_double_expert_net_train/古页1_for_松页油1_泉头组_double_expert_net_DTXX_3_layers__lr_0.005h_dim12_epoch_30.json\n"
     ]
    }
   ],
   "source": [
    "# 定义模型保存的json_name\n",
    "json_name = train_well_name  + \"_for_\" + test_well_name + \"_\" + layer_name + \"_\" + model_type.lower() + \"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "        learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS) + \".json\"\n",
    "model_json = model_path + json_name\n",
    "print(model_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Test operation loading model storage location（测试操作加载模型存放位置)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"test\":   \n",
    "    pred_model_json = model_json\n",
    "    pred_model_file = model_file\n",
    "    # custom_model_json =  \"train_all_blstm_\"+ element_name + \"_4_layers__lr_0.005h_dim30_epoch_40.json\"\n",
    "    # custom_model_file =  \"train_all_blstm_\"+ element_name + \"_4_layers__lr_0.005h_dim30_epoch_40.h5\"\n",
    "    \n",
    "    # pred_model_json = os.path.join(model_path,custom_model_json)\n",
    "    # pred_model_file = os.path.join(model_path,custom_model_file)\n",
    "    \n",
    "    if not (os.path.exists(pred_model_json) and os.path.exists(pred_model_file)):\n",
    "        print(\"预测模型不存在，程序结束，请训练相应模型\")\n",
    "        exit()\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Define the location for saving algorithm results, charts, and text（定义算法结果图表文字保存位置）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    # well_name = filename_AB.split(\"_\")[0]\n",
    "    # begin_depth = depth_log[0][0]\n",
    "    # end_depth = depth_log[-1][0]\n",
    "    \n",
    "    training_img_file_saving_path = 'curve_reconstract_model_training_images/'\n",
    "    model_training_img_file_saving_path = os.path.join(training_img_file_saving_path,child_dir_name)\n",
    "    model_training_img_name =  layer_name + \"_\" + model_type + \"_\" + well_name + \"_\"+ element_name\n",
    "    \n",
    "    if not os.path.exists(model_training_img_file_saving_path):\n",
    "        os.mkdir(model_training_img_file_saving_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"test\": \n",
    "     \n",
    "    model_testing_image_name =  layer_name + \"_\" + model_type + \"_\" + well_name + \"_\" + element_name\n",
    "    model_testing_img_file_saving_path = 'curve_reconstract_model_testing_images/'\n",
    "    if not os.path.exists(model_testing_img_file_saving_path):\n",
    "        os.mkdir(model_testing_img_file_saving_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "font={'family':'SimHei',\n",
    "     'style':'italic',\n",
    "    'weight':'normal',\n",
    "      'color':'red',\n",
    "      'size':16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curve_reconstract_csv_results/double_expert_net_test/\n"
     ]
    }
   ],
   "source": [
    "csv_file_saving_path = \"curve_reconstract_csv_results/\"\n",
    "if model_stage == \"test\": \n",
    "    csv_file_saving_path = os.path.join(\"curve_reconstract_csv_results/\", model_type.lower() + \"_test/\")\n",
    "else:\n",
    "    csv_file_saving_path = os.path.join(\"curve_reconstract_csv_results/\", model_type.lower() + \"_train/\")\n",
    "if not os.path.exists(csv_file_saving_path):\n",
    "    os.mkdir(csv_file_saving_path)\n",
    "print(csv_file_saving_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Data loading and processing（数据加载及处理）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "调用pandas的read_csv()方法时，默认使用C engine作为parser engine，而当文件名中含有中文的时候，用C engine在部分情况下就会出错。所以在调用read_csv()方法时指定engine为Python就可以解决问题了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data loading....\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"train\":\n",
    "    AB_use = pd.read_csv(TrainDataPath,engine='python',encoding='GBK')\n",
    "    AB_use  = AB_use.dropna()\n",
    "    print(\"Training data loading....\")\n",
    "else:\n",
    "    A_read = pd.read_csv(TestDataPath,engine='python',encoding='GBK')\n",
    "    A_read  = A_read.dropna()\n",
    "    print(\"Testing data loading....\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"train\":\n",
    "    # print(AB_use)\n",
    "    print(len(AB_use.columns))\n",
    "else:\n",
    "    # print(A_read)\n",
    "    print(len(A_read.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin_depth 2449.0 end_depth 2529.0\n",
      "use_depth_log: True\n"
     ]
    }
   ],
   "source": [
    "use_depth_log = False\n",
    "if model_stage == \"train\":\n",
    "    # 绘制训练段曲线\n",
    "    Y_ele = AB_use.loc[:, element]\n",
    "    if AB_use.columns.values[0] == \"DEPTH\":\n",
    "        use_depth_log = True\n",
    "        depth_log = AB_use.loc[:, [\"DEPTH\"]]\n",
    "        depth_log = np.array(depth_log)\n",
    "        cucao_depth = np.array(depth_log)\n",
    "        cucao_depth.shape = (len(cucao_depth),)\n",
    "        begin_depth = depth_log[0][0]\n",
    "        end_depth = depth_log[-1][0]\n",
    "        print(\"begin_depth\",begin_depth,\"end_depth\",end_depth)\n",
    "        plt.figure(figsize=(4,16))\n",
    "        plt.title(\"element_log\")\n",
    "        plt.plot(Y_ele[element_name],depth_log,color=\"red\", label=element_name)\n",
    "        plt.xlabel(element_name + \"(%)\")\n",
    "        plt.ylabel(\"DEPTH(m)\")\n",
    "        plt.legend(loc='best')\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"No Depth Information\")\n",
    "else:\n",
    "    if A_read.columns.values[0] == \"DEPTH\":\n",
    "        use_depth_log = True\n",
    "        depth_log = A_read.loc[:, [\"DEPTH\"]]\n",
    "        depth_log = np.array(depth_log)\n",
    "        cucao_depth = np.array(depth_log)\n",
    "        cucao_depth.shape = (len(cucao_depth),)\n",
    "        begin_depth = depth_log[seq_length][0]\n",
    "        end_depth = depth_log[-1][0]\n",
    "        print(\"begin_depth\",begin_depth,\"end_depth\",end_depth)\n",
    "    else:\n",
    "        print(\"No Depth Information,all method is end!!!\")\n",
    "        # exit()\n",
    "print(\"use_depth_log:\",use_depth_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Set independent variable and dependent variable data（设置自变量应变量数据）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Take logarithmic value of resistivity curve（电阻率曲线取对数值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    inputY = AB_use.loc[:, element]\n",
    "    inputY_calc = AB_use.loc[:, reference]\n",
    "    inputX = AB_use.loc[:,input_vectors]\n",
    "    print(inputY)\n",
    "    print(inputY_calc)\n",
    "    if train_use_weight == False:\n",
    "        sample_weight = np.ones(len(inputY))\n",
    "        # print(AB_Y)\n",
    "    else:\n",
    "        # 设定权重矩阵\n",
    "        sample_weight = AB_use.loc[:, weight_coloum]\n",
    "else:\n",
    "    inputX = A_read.loc[:,input_vectors]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 电阻率取对数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RD\n",
      "RS\n"
     ]
    }
   ],
   "source": [
    "electric_log = [\"RD\",\"RS\",\"RLLD\",\"RLLS\"]\n",
    "for item in electric_log:\n",
    "    if item in input_vectors:\n",
    "        print(item)\n",
    "        for i in range(len(inputX)):\n",
    "            if (inputX.loc[:,item][i]) <= 0.01:\n",
    "                inputX.loc[:,item][i] = 0.01 \n",
    "        inputX.loc[:,item] = np.log10(inputX.loc[:,item])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Normalization operations(归一化操作)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 设置输入曲线范围,由于取了对数，所以RD和RS设置下限-1\n",
    "AC = [140,350]\n",
    "DT = [40,200]\n",
    "# AC = [140,300]\n",
    "AC = [40,300]\n",
    "CNL = [-0.8,70]\n",
    "DEN = [1,3]\n",
    "GR = [0,350]\n",
    "RD = [0,5]\n",
    "RS = [0,5]\n",
    "RLLD = [0,5]\n",
    "RLLS = [0,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "DTXX = [100,300]\n",
    "DTC = [100,300]\n",
    "DTS = [100,300]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "创建字典，根据输入的数据维度调正归一化的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# u_log = {\"AC\": AC, \"CNL\": CNL, \"DEN\": DEN, \"GR\": GR, \"RD\": RD, \"RS\": RS,\"RLLD\": RLLD, \"RLLS\": RLLS}\n",
    "# e_log = {\"孔隙度\": POR, \"饱和度\": SW, \"渗透率\": PERM}\n",
    "# e_CALC_log = {\"POR\": POR_CALC, \"SW\": SW_CALC, \"PERM\": PERM_CALC}\n",
    "\n",
    "u_log = {\"DT\":DT,\"AC\": AC,\"CNL\": CNL, \"DEN\": DEN, \"GR\": GR, \"RD\": RD, \"RS\": RS,\"RLLD\": RLLD, \"RLLS\": RLLS}\n",
    "e_log = {\"DTS\":DTS,\"DTXX\":DTXX,\"AC\": AC, \"CNL\": CNL, \"DEN\": DEN, \"GR\": GR, \"RD\": RD, \"RS\": RS,\"RLLD\": RLLD, \"RLLS\": RLLS}\n",
    "e_CALC_log = {\"DT\":DT,\"DTXX\":DTXX,\"DTS\":DTS,\"DTC\":DTC,\"AC\": AC, \"CNL\": CNL, \"DEN\": DEN, \"GR\": GR, \"RD\": RD, \"RS\": RS,\"RLLD\": RLLD, \"RLLS\": RLLS}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[40, 200], [-0.8, 70], [1, 3], [0, 350], [0, 5], [0, 5]]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_log_name = []\n",
    "# 关键在于 input_vectors''\n",
    "for i in input_vectors:\n",
    "    u_log_name.append(u_log[i])\n",
    "    \n",
    "u_log_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[100, 300]]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_log_name = []\n",
    "for i in element:\n",
    "    e_log_name.append(e_log[i])\n",
    "e_log_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[100, 300]]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_calc_log_name = []\n",
    "for i in reference:\n",
    "    e_calc_log_name.append(e_CALC_log[i])\n",
    "e_calc_log_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# # 定义归一化函数--senutil\n",
    "# def zero_one_scaler(data,log_name):\n",
    "#     ''' Normalization'''\n",
    "#     result = data.copy()\n",
    "#     for i in range(len(log_name)):\n",
    "#         numerator = data.iloc[:,i]-log_name[i][0]\n",
    "#         denominator = log_name[i][1]-log_name[i][0]\n",
    "#         result.iloc[:,i]= numerator / (denominator + 1e-7)\n",
    "#     return  result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#inputY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(649, 6)\n",
      "zero_one_scaler is finished!\n"
     ]
    }
   ],
   "source": [
    "if operate_standard == True:\n",
    "    AB_G = sen.zero_one_scaler(inputX,u_log_name)\n",
    "    print(AB_G.shape)\n",
    "\n",
    "    if model_stage == \"train\":\n",
    "        inputY_train = sen.zero_one_scaler(inputY,e_log_name)\n",
    "        inputY_calc_train = sen.zero_one_scaler(inputY_calc,e_calc_log_name)\n",
    "        AB_Y_G = inputY_train.loc[:,[element_name]]\n",
    "        AB_Y_calc_G = inputY_calc_train.loc[:,[reference_name]]\n",
    "        print(AB_Y_G.shape)\n",
    "        print(AB_Y_calc_G.shape)\n",
    "    print(\"zero_one_scaler is finished!\")\n",
    "else:\n",
    "    AB_G = inputX\n",
    "    if model_stage == \"train\":\n",
    "        inputY_train = inputY\n",
    "        inputY_calc_train = inputY_calc\n",
    "        AB_Y_G = inputY_train.loc[:,[element_name]]\n",
    "        AB_Y_calc_G = inputY_calc_train.loc[:,[reference_name]]\n",
    "        print(AB_Y_G.shape)\n",
    "        print(AB_Y_calc_G.shape)\n",
    "    print(\"data preparation is finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# print(AB_G)\n",
    "# AB_Y_G.loc[:, [element_name]]\n",
    "if model_stage == \"train\":\n",
    "# len(AB_Y_G.loc[:, [element_name]])\n",
    "# AB_Y_G_Smooth = sen.fast_moving_average(AB_Y_G.loc[:, [element_name]],1)\n",
    "    cu = AB_Y_G.loc[:, [element_name]]\n",
    "# cu.shape = (len(cu),)\n",
    "    cucao = np.array(cu)\n",
    "    print(type(cucao),cucao.shape)\n",
    "    cucao.shape = (len(cucao),)\n",
    "    AB_Y_G_Smooth = sen.fast_moving_average(cucao,5)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,20))\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.title(\"输入维度\")\n",
    "sample_index = np.arange(len(AB_G))\n",
    "for i in range(len(input_vectors)):\n",
    "    plt.plot(AB_G[input_vectors[i]],sample_index, label=input_vectors[i])\n",
    "plt.xlabel(\"归一化后值\")\n",
    "plt.ylabel(\"样本点编号\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid(True)\n",
    "\n",
    "if model_stage == \"train\":\n",
    "    plt.subplot(132)\n",
    "    plt.title(\"目标维度\")\n",
    "    plt.plot(AB_Y_G,sample_index, color = \"green\", label = element_name)\n",
    "    plt.xlabel(\"归一化后值\")\n",
    "    plt.ylabel(\"样本点编号\")\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplot(133)\n",
    "    plt.title(\"计算的目标维度\")\n",
    "    plt.plot(AB_Y_calc_G,sample_index,color = \"blue\", label = element_name + \"_clac\")\n",
    "    plt.xlabel(\"归一化后值\")\n",
    "    plt.ylabel(\"样本点编号\")\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.grid(True)\n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name +  '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "            learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_tendency.png', dpi=96,  bbox_inches='tight')\n",
    "\n",
    "if model_stage == \"test\":\n",
    "    plt.savefig(model_testing_img_file_saving_path + model_testing_image_name +\"_\" + element_name  + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "            learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_tendency.png', dpi=96,  bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "element_flag = element.index(element_name)\n",
    "if model_stage == \"train\":\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.title(\"relationship\")\n",
    "    plt.scatter(AB_Y_calc_G,AB_Y_G)\n",
    "    plt.xlabel(reference[element_flag])\n",
    "    plt.ylabel(element[element_flag])\n",
    "    plt.xlim(-0.05,1.05)\n",
    "    plt.ylim(-0.05,1.05)\n",
    "    #显示网格线\"\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    # plt.savefig(model_testing_img_file_saving_path + model_testing_image_name + 'ValAll.jpg', dpi=220,  bbox_inches='tight')\n",
    "\n",
    "#     print(\"You Input a wrong Target Parameter!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Input the independent variables data preparation(输入自变量数据准备)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    AB_Y = np.array(AB_Y_G)\n",
    "    AB_Y_calc = np.array(AB_Y_calc_G) \n",
    "AB_X = np.array(AB_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# sample_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Split the training set and the validation set(训练集验证集划分)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Determine whether serialization is required based on the needs of the model\n",
    "(根据模型需要判定是否需要序列化)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "序列化\n"
     ]
    }
   ],
   "source": [
    "if (flag == 1) or (flag == 2):\n",
    "    print(\"不需要序列化\")\n",
    "    if model_stage == \"train\":\n",
    "        # 训练阶段\n",
    "        dataX = AB_X\n",
    "        dataY = AB_Y\n",
    "        dataY_calc = AB_Y_calc\n",
    "        sss = model_selection.ShuffleSplit(n_splits=10, test_size=0.1, random_state=0)\n",
    "        for train_index, test_index in sss.split(dataX):\n",
    "        # print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "            train_X, test_X = dataX[train_index], dataX[test_index]\n",
    "            train_Y, test_Y = dataY[train_index], dataY[test_index]\n",
    "            train_Y_calc, test_Y_calc = dataY_calc[train_index], dataY_calc[test_index]\n",
    "            train_weight, test_weight = sample_weight[train_index],sample_weight[test_index]\n",
    "    else:\n",
    "        # 测试阶段\n",
    "        testALL_A_X = AB_X\n",
    "    if use_depth_log == True:\n",
    "        DEPTH_AddReslution = depth_log\n",
    "    else:\n",
    "        DEPTH_AddReslution = None\n",
    "else:\n",
    "    print(\"序列化\")\n",
    "    if model_stage == \"train\":\n",
    "#         if (flag == 1) or (flag == 2):\n",
    "#             print(\"不需要序列化\")\n",
    "#             dataX = AB_X\n",
    "#             dataY = AB_Y\n",
    "#             dataY_calc = AB_Y_calc\n",
    "#         else:\n",
    "        dataX, dataY = sen.build_All_Train_dataset(AB_X, AB_Y, seq_length)\n",
    "        dataY_calc = sen.build_All_Y_dataset( AB_Y_calc, seq_length)\n",
    "        weight_matrix = sen.build_All_Y_dataset(sample_weight,seq_length)\n",
    "            # 使用model_selection.ShuffleSplit抽取样本\n",
    "        sss = model_selection.ShuffleSplit(n_splits = 10, test_size=0.1, random_state=0)\n",
    "        for train_index, test_index in sss.split(dataX):\n",
    "        # print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "            trainX, testX = dataX[train_index], dataX[test_index]\n",
    "            trainY, testY = dataY[train_index], dataY[test_index]\n",
    "            train_Y_calc, test_Y_calc = dataY_calc[train_index], dataY_calc[test_index]\n",
    "            train_weight, test_weight = weight_matrix[train_index],weight_matrix[test_index]\n",
    "            \n",
    "    else:\n",
    "        # 测试阶段\n",
    "        testALL_A_X = sen.build_All_A_dataset(AB_X, seq_length)\n",
    "    if use_depth_log == True:\n",
    "        DEPTH_AddReslution = sen.build_addReslution_DEPTH(depth_log, seq_length)\n",
    "    else:\n",
    "        DEPTH_AddReslution = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Confirm the input dimension(确认输入维度)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testALL_A_X.shape: (641, 8, 6) \n",
      " input_vectors.length: 6\n"
     ]
    }
   ],
   "source": [
    "if (flag == 1) or (flag == 2):\n",
    "    if model_stage == \"train\":\n",
    "        print(\"input_vectors.length:\",len(input_vectors))\n",
    "        print(\"train_X.shape:\", train_X.shape,\"test_X.shape:\", test_X.shape)\n",
    "        print(\"train_Y.shape:\", train_Y.shape,\"test_Y.shape:\", test_Y.shape)\n",
    "        print(\"train_Y_calc.shape:\", train_Y_calc.shape,\"test_Y_calc.shape:\", test_Y_calc.shape)\n",
    "        print(\"train_weight.shape:\", train_weight.shape,\"test_weight.shape:\", test_weight.shape)\n",
    "    else:\n",
    "        print(\"testALL_A_X.shape:\", testALL_A_X.shape,\"\\n\",\"input_vectors.length:\",len(input_vectors))\n",
    "else:\n",
    "    if model_stage == \"train\":\n",
    "        print(\"input_vectors.length:\",len(input_vectors))\n",
    "        print(\"trainX.shape:\", trainX.shape,\"testX.shape:\", testX.shape) \n",
    "        print(\"trainY.shape:\", trainY.shape,\"testY.shape:\", testY.shape)\n",
    "        print(\"train_Y_calc.shape:\", train_Y_calc.shape,\"test_Y_calc.shape:\", test_Y_calc.shape)\n",
    "        print(\"train_weight.shape:\", train_weight.shape,\"test_weight.shape:\", test_weight.shape)\n",
    "    else:\n",
    "        print(\"testALL_A_X.shape:\", testALL_A_X.shape,\"\\n\",\"input_vectors.length:\",len(input_vectors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# train_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Network instantiation(网络实例化)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Build a network or load a model(构建网络或载入模型)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def model_type_select(model_type):\n",
    "    if model_type == 'DNN':\n",
    "        return sms.dnn_model(model_para)\n",
    "    elif model_type == 'DNN_2':\n",
    "        return sms.dnn_model_2(model_para)\n",
    "    elif model_type == 'RBF':\n",
    "        n_layers = 3\n",
    "        # return sms.rbf_model(train_X,model_para)\n",
    "        return sms.rbf_model(model_para)\n",
    "    elif model_type == 'LSTM':\n",
    "        return sms.lstm_cell_model(model_para)\n",
    "    elif model_type == 'GRU':\n",
    "        return sms.gru_cell_model(model_para)\n",
    "    elif model_type == 'GRU2':\n",
    "        return sms.gru_block_cell_2(model_para)\n",
    "    # elif model_type == 'NAS':\n",
    "    #     return nas_cell()\n",
    "    elif model_type == 'BLSTM':\n",
    "        return sms.bi_lstm_cell_model(model_para)\n",
    "    elif model_type == 'BGRU':\n",
    "        return sms.bi_gru_cell_model(model_para)\n",
    "    elif model_type == 'BiGRU-Atten':\n",
    "        return sms.bigru_atten_model(model_para)\n",
    "    elif model_type == 'BiGRU-Atten2':\n",
    "        return sms.bigru_atten_model_2(model_para)\n",
    "    elif model_type == 'WaveNet':\n",
    "        return sms.wavenet_model(model_para)\n",
    "    elif model_type == 'MyWaveNet':\n",
    "        return sms.wavenet_model2(model_para)\n",
    "    elif model_type == 'Double_Expert_Net':\n",
    "        return sms.double_experts_Net_model(model_para)\n",
    "    elif model_type == 'MyUNet':\n",
    "        return sms.my_unet(model_para)\n",
    "    elif model_type == 'Capsule':\n",
    "        return sms.bilstm_capsule_model(model_para)\n",
    "    elif model_type == 'BLSTM-Atten':\n",
    "        return sms.bilstm_atten_model(model_para)\n",
    "    elif model_type == 'BiLSTM-Atten2':\n",
    "        return sms.bilstm_atten_model_2(model_para)\n",
    "    elif model_type == 'BiLSTM-Atten3':\n",
    "        return sms.bilstm_atten_model_3(model_para)\n",
    "    elif model_type == 'BiLSTM-Atten4':\n",
    "        return sms.bilstm_atten_model_4(model_para)\n",
    "    elif model_type == 'BiLSTM-Atten5':\n",
    "        return sms.bilstm_atten_model_5(model_para)\n",
    "    elif model_type == 'BiLSTM-Atten6':\n",
    "        return sms.bilstm_atten_model_6(model_para)\n",
    "    elif model_type == 'CNN_Atten':\n",
    "        return sms.wavenet_atten_model(model_para)\n",
    "    else:\n",
    "        return sms.bi_lstm_cell_model(model_para)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curve_reconstract_model/泉头组_sampleweight_double_expert_net_train/古页1_for_松页油1_泉头组_double_expert_net_DTXX_3_layers__lr_0.005h_dim12_epoch_30.h5\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"test\":\n",
    "    print(pred_model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# from attention_layer import AttentionLayer\n",
    "# from tensorflow.keras import backend as Kbackend\n",
    "# from tensorflow.keras.layers import Activation,Lambda, Multiply, Add, Concatenate\n",
    "# import tensorflow as tf\n",
    "# # import keras\n",
    "# from tensorflow.keras.layers import Layer\n",
    "# from tensorflow.keras import initializers\n",
    "# from tensorflow.keras.initializers import RandomUniform, Initializer, Constant\n",
    "# from tensorflow import keras\n",
    "# from tensorflow.keras import layers\n",
    "# # from keras_self_attention import SeqSelfAttention\n",
    "# import numpy as np\n",
    "# from sklearn.cluster import KMeans\n",
    "# from tensorflow.keras.models import Model\n",
    "# from tensorflow.keras.layers import Input, Conv1D, Dense, Activation, Dropout, Lambda, Multiply, Add, Concatenate\n",
    "# from tensorflow.keras.layers import GlobalAveragePooling1D, MaxPool1D, GlobalMaxPool1D, UpSampling1D\n",
    "# from tensorflow.keras.layers import Concatenate,concatenate\n",
    "\n",
    "# def wavenet_atten_model(model_parameter):\n",
    "#     # convolutional operation parameters\n",
    "#     n_filters = model_parameter.hidden_dim # 32 \n",
    "#     filter_width = 2\n",
    "#     # dilation_rates = [2**i for i in range(8)] * 2   # \n",
    "#     # dilation_rates = [2**i for i in range(model_parameter.seq_length)] * 2\n",
    "#     dilation_rates = [2**i for i in range(model_parameter.seq_length)] * 2 \n",
    "    \n",
    "#     # define an input history series and pass it through a stack of dilated causal convolution blocks. \n",
    "#     history_seq = Input(shape=(model_parameter.seq_length, model_parameter.data_dim))\n",
    "#     x = history_seq\n",
    "    \n",
    "#     # 增加正则化层-2020-03-20\n",
    "#     x = tf.keras.layers.BatchNormalization()(x)\n",
    "    \n",
    "#     skips = []\n",
    "#     for dilation_rate in dilation_rates:\n",
    "        \n",
    "#         # preprocessing - equivalent to time-distributed dense\n",
    "#         # x = Conv1D(16, 1, padding='same')(x) data_dim\n",
    "#         # x = Conv1D(model_parameter.seq_length * 2, 1, padding='same')(x)\n",
    "#         x = Conv1D(model_parameter.data_dim * 4, 1, padding='same')(x)\n",
    " \n",
    "        \n",
    "#         # filter convolution\n",
    "#         x_f = Conv1D(filters=n_filters,\n",
    "#                     kernel_size=filter_width, \n",
    "#                     padding='causal',\n",
    "#                     dilation_rate=dilation_rate)(x)\n",
    "        \n",
    "#         # gating convolution\n",
    "#         x_g = Conv1D(filters=n_filters,\n",
    "#                     kernel_size=filter_width, \n",
    "#                     padding='causal',\n",
    "#                     dilation_rate=dilation_rate)(x)\n",
    "        \n",
    "#         # multiply filter and gating branches\n",
    "#         # z = Multiply()([Activation('tanh')(x_f),\n",
    "#         #                Activation('sigmoid')(x_g)])\n",
    "#         z = Multiply()([Activation('tanh')(x_f),\n",
    "#                         Activation('relu')(x_g)])\n",
    "        \n",
    "#         # postprocessing - equivalent to time-distributed dense\n",
    "#         # z = Conv1D(16, 1, padding='same')(z)\n",
    "#         # z = Conv1D(model_parameter.seq_length * 2, 1, padding='same')(z)\n",
    "#         z = Conv1D(model_parameter.data_dim * 4, 1, padding='same')(z)\n",
    "        \n",
    "        \n",
    "#         # residual connection\n",
    "#         x = Add()([x, z])\n",
    "\n",
    "# #         repeated_word_attention = tf.keras.layers.RepeatVector(model_parameter.hidden_dim * 4)(attention_layer)\n",
    "# #         repeated_word_attention = tf.keras.layers.Permute([2, 1])(repeated_word_attention)\n",
    "# #         sentence_representation = tf.keras.layers.Multiply()([z, repeated_word_attention])\n",
    "    \n",
    "# #         # sentence_representation = tf.keras.layers.Lambda(lambda x: Kbackend.sum(x, axis=1))(sentence_representation)\n",
    "# #         # skips.append(sentence_representation) \n",
    "# #         z = tf.keras.layers.Lambda(lambda x: Kbackend.sum(x, axis=1))(sentence_representation)\n",
    "#         # collect skip connections\n",
    "#         skips.append(z)\n",
    "\n",
    "#     # add all skip connection outputs \n",
    "# #     out = Activation('relu')(Concatenate()(skips))\n",
    "# #     out = Activation('relu')(Add()(skips))\n",
    "#     out = Add()(skips)\n",
    "\n",
    "#     # final time-distributed dense layers \n",
    "#     # out = Conv1D(128, 1, padding='same')(out)\n",
    "#     out = Conv1D(model_parameter.data_dim * 2, 1, padding='same')(out)\n",
    "#     # out = Activation('relu')(out)\n",
    "#     out = Dropout(model_parameter.dropout_rate)(out)\n",
    "#     out = Conv1D(1, 1, padding='same')(out)\n",
    "#     out = GlobalAveragePooling1D()(out)\n",
    "# #     out = AttentionLayer()(out)\n",
    "    \n",
    "#     pred_seq_train = layers.Dense(model_parameter.output_dim)(out)\n",
    "#     # pred_seq_train = Dropout(model_parameter.dropout_rate)(pred_seq_train)\n",
    "\n",
    "#     # extract the last 60 time steps as the training target\n",
    "#     # def slice(x, seq_length):\n",
    "#     #     return x[:,-seq_length:,:]\n",
    "\n",
    "#     # pred_seq_train = Lambda(slice, arguments={'seq_length':model_parameter.seq_length})(out)\n",
    "\n",
    "#     model = Model(history_seq, pred_seq_train)\n",
    "    \n",
    "#     optimizer = tf.keras.optimizers.Adam(model_parameter.learning_rate)\n",
    "#     # optimizer = tf.keras.optimizers.RMSprop(model_parameter.learning_rate)\n",
    "#     # loss = 'mse', 'mean_squared_error', 'huber_loss'\n",
    "#     my_loss = 'mse'\n",
    "#     model.compile(optimizer=optimizer,\n",
    "#                   loss = my_loss,\n",
    "#                   metrics=['mae', 'mse'])\n",
    "#     return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Model visualization(模型可视化)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 8, 6)]       0           []                               \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 8, 6)        24          ['input_1[0][0]']                \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 8, 6)        24          ['batch_normalization[0][0]']    \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)                (None, 8, 8)         56          ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)              (None, 8, 12)        204         ['conv1d[0][0]']                 \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)              (None, 8, 12)        204         ['conv1d[0][0]']                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 8, 12)        0           ['conv1d_1[0][0]']               \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 8, 12)        0           ['conv1d_2[0][0]']               \n",
      "                                                                                                  \n",
      " multiply (Multiply)            (None, 8, 12)        0           ['activation[0][0]',             \n",
      "                                                                  'activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_3 (Conv1D)              (None, 8, 8)         104         ['multiply[0][0]']               \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 8, 8)         0           ['conv1d[0][0]',                 \n",
      "                                                                  'conv1d_3[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_4 (Conv1D)              (None, 8, 8)         72          ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " conv1d_5 (Conv1D)              (None, 8, 12)        204         ['conv1d_4[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_6 (Conv1D)              (None, 8, 12)        204         ['conv1d_4[0][0]']               \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 8, 12)        0           ['conv1d_5[0][0]']               \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 8, 12)        0           ['conv1d_6[0][0]']               \n",
      "                                                                                                  \n",
      " multiply_1 (Multiply)          (None, 8, 12)        0           ['activation_2[0][0]',           \n",
      "                                                                  'activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_7 (Conv1D)              (None, 8, 8)         104         ['multiply_1[0][0]']             \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 8, 8)         0           ['conv1d_4[0][0]',               \n",
      "                                                                  'conv1d_7[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_8 (Conv1D)              (None, 8, 8)         72          ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_9 (Conv1D)              (None, 8, 12)        204         ['conv1d_8[0][0]']               \n",
      "                                                                                                  \n",
      " conv1d_10 (Conv1D)             (None, 8, 12)        204         ['conv1d_8[0][0]']               \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 8, 12)        0           ['conv1d_9[0][0]']               \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 8, 12)        0           ['conv1d_10[0][0]']              \n",
      "                                                                                                  \n",
      " multiply_2 (Multiply)          (None, 8, 12)        0           ['activation_4[0][0]',           \n",
      "                                                                  'activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_11 (Conv1D)             (None, 8, 8)         104         ['multiply_2[0][0]']             \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 8, 8)         0           ['conv1d_8[0][0]',               \n",
      "                                                                  'conv1d_11[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_12 (Conv1D)             (None, 8, 8)         72          ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_13 (Conv1D)             (None, 8, 12)        204         ['conv1d_12[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_14 (Conv1D)             (None, 8, 12)        204         ['conv1d_12[0][0]']              \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 8, 12)        0           ['conv1d_13[0][0]']              \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 8, 12)        0           ['conv1d_14[0][0]']              \n",
      "                                                                                                  \n",
      " multiply_3 (Multiply)          (None, 8, 12)        0           ['activation_6[0][0]',           \n",
      "                                                                  'activation_7[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_15 (Conv1D)             (None, 8, 8)         104         ['multiply_3[0][0]']             \n",
      "                                                                                                  \n",
      " add_3 (Add)                    (None, 8, 8)         0           ['conv1d_12[0][0]',              \n",
      "                                                                  'conv1d_15[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_16 (Conv1D)             (None, 8, 8)         72          ['add_3[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_17 (Conv1D)             (None, 8, 12)        204         ['conv1d_16[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_18 (Conv1D)             (None, 8, 12)        204         ['conv1d_16[0][0]']              \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 8, 12)        0           ['conv1d_17[0][0]']              \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 8, 12)        0           ['conv1d_18[0][0]']              \n",
      "                                                                                                  \n",
      " multiply_4 (Multiply)          (None, 8, 12)        0           ['activation_8[0][0]',           \n",
      "                                                                  'activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_19 (Conv1D)             (None, 8, 8)         104         ['multiply_4[0][0]']             \n",
      "                                                                                                  \n",
      " add_4 (Add)                    (None, 8, 8)         0           ['conv1d_16[0][0]',              \n",
      "                                                                  'conv1d_19[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_20 (Conv1D)             (None, 8, 8)         72          ['add_4[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_21 (Conv1D)             (None, 8, 12)        204         ['conv1d_20[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_22 (Conv1D)             (None, 8, 12)        204         ['conv1d_20[0][0]']              \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 8, 12)        0           ['conv1d_21[0][0]']              \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 8, 12)        0           ['conv1d_22[0][0]']              \n",
      "                                                                                                  \n",
      " multiply_5 (Multiply)          (None, 8, 12)        0           ['activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_23 (Conv1D)             (None, 8, 8)         104         ['multiply_5[0][0]']             \n",
      "                                                                                                  \n",
      " add_5 (Add)                    (None, 8, 8)         0           ['conv1d_20[0][0]',              \n",
      "                                                                  'conv1d_23[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_24 (Conv1D)             (None, 8, 8)         72          ['add_5[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_25 (Conv1D)             (None, 8, 12)        204         ['conv1d_24[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_26 (Conv1D)             (None, 8, 12)        204         ['conv1d_24[0][0]']              \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 8, 12)        0           ['conv1d_25[0][0]']              \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 8, 12)        0           ['conv1d_26[0][0]']              \n",
      "                                                                                                  \n",
      " multiply_6 (Multiply)          (None, 8, 12)        0           ['activation_12[0][0]',          \n",
      "                                                                  'activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_27 (Conv1D)             (None, 8, 8)         104         ['multiply_6[0][0]']             \n",
      "                                                                                                  \n",
      " add_6 (Add)                    (None, 8, 8)         0           ['conv1d_24[0][0]',              \n",
      "                                                                  'conv1d_27[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_28 (Conv1D)             (None, 8, 8)         72          ['add_6[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_29 (Conv1D)             (None, 8, 12)        204         ['conv1d_28[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_30 (Conv1D)             (None, 8, 12)        204         ['conv1d_28[0][0]']              \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 8, 12)        0           ['conv1d_29[0][0]']              \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 8, 12)        0           ['conv1d_30[0][0]']              \n",
      "                                                                                                  \n",
      " multiply_7 (Multiply)          (None, 8, 12)        0           ['activation_14[0][0]',          \n",
      "                                                                  'activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_31 (Conv1D)             (None, 8, 8)         104         ['multiply_7[0][0]']             \n",
      "                                                                                                  \n",
      " add_8 (Add)                    (None, 8, 8)         0           ['conv1d_3[0][0]',               \n",
      "                                                                  'conv1d_7[0][0]',               \n",
      "                                                                  'conv1d_11[0][0]',              \n",
      "                                                                  'conv1d_15[0][0]',              \n",
      "                                                                  'conv1d_19[0][0]',              \n",
      "                                                                  'conv1d_23[0][0]',              \n",
      "                                                                  'conv1d_27[0][0]',              \n",
      "                                                                  'conv1d_31[0][0]']              \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 8, 8)         0           ['add_8[0][0]']                  \n",
      "                                                                                                  \n",
      " conv1d_32 (Conv1D)             (None, 8, 12)        108         ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 8, 12)        0           ['conv1d_32[0][0]']              \n",
      "                                                                                                  \n",
      " bidirectional (Bidirectional)  (None, 8, 24)        1824        ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv1d_33 (Conv1D)             (None, 8, 1)         13          ['activation_17[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_1 (Bidirectional  (None, 24)          3552        ['bidirectional[0][0]']          \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " global_average_pooling1d (Glob  (None, 1)           0           ['conv1d_33[0][0]']              \n",
      " alAveragePooling1D)                                                                              \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 1)            25          ['bidirectional_1[0][0]']        \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 1)            2           ['global_average_pooling1d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 2)            0           ['dense[0][0]',                  \n",
      "                                                                  'dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1)            3           ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 10,231\n",
      "Trainable params: 10,207\n",
      "Non-trainable params: 24\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Model()\n",
    "if model_stage == \"train\":\n",
    "    model =  model_type_select(model_type)\n",
    "#     model = wavenet_atten_model(model_para)\n",
    "    # model = sms.bi_lstm_cell_model(model_para)\n",
    "    # model = bilstm_capsule_model(model_para)\n",
    "else:\n",
    "    with open(pred_model_json, \"r\") as json_file_1:\n",
    "        json_config_1 = json_file_1.read()\n",
    "        # 此处加载模型无需判断 \n",
    "        model = tf.keras.models.model_from_json(json_config_1,custom_objects={'RBFLayer': sms.RBFLayer,\n",
    "                                                                              'AttentionLayer':sms.AttentionLayer,\n",
    "                                                                              'GlorotUniform':tf.keras.initializers.GlorotUniform\n",
    "                                                                              })\n",
    "        model.load_weights(pred_model_file)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 定义结果打印函数\n",
    "class PrintDot(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        if epoch % 10 == 0:\n",
    "            print('已经训练完'+ epoch +'Epoch')\n",
    "            print('.', end='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#if  pg.find_graphviz() is not None:\n",
    "if model_stage == \"train\":\n",
    "    model_image_path = model_path\n",
    "else:\n",
    "    model_image_path = csv_file_saving_path\n",
    "if plot_modelnet == True:\n",
    "    tf.keras.utils.plot_model(model,to_file= model_image_path + element_name + '_' + model_type + '_net.png',\n",
    "               show_shapes=True,\n",
    "               show_layer_names=True,\n",
    "               rankdir='TB',\n",
    "               expand_nested=False,\n",
    "               dpi=96)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## The log storage content is set(日志保存内容设定)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_log_dir: curve_reconstract_logs/double_expert_net\n",
      "curve_reconstract_logs_child_dir: curve_reconstract_logs/double_expert_net\\1682067674\n"
     ]
    }
   ],
   "source": [
    "current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "\n",
    "if not os.path.exists(log_path):\n",
    "    # 针对第一次训练\n",
    "    os.makedirs(log_path)\n",
    "my_log_dir = os.path.join(log_path,model_type.lower())\n",
    "print(\"my_log_dir:\",my_log_dir)\n",
    "if not os.path.exists(my_log_dir):\n",
    "    os.mkdir(my_log_dir)\n",
    "\n",
    "str_name = model_name.split(\".h5\")[0]\n",
    "# log_name = str_name + \"-{}\".format(int(time.time()))\n",
    "log_name = \"{}\".format(int(time.time()))\n",
    "curve_reconstract_logs_child_dir = os.path.join(my_log_dir,log_name)\n",
    "print(\"curve_reconstract_logs_child_dir:\",curve_reconstract_logs_child_dir)\n",
    "if not os.path.exists(curve_reconstract_logs_child_dir):\n",
    "    os.mkdir(curve_reconstract_logs_child_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Model training and testing(模型训练与测试)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Model training and validation（模型训练与验证）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "fit函数解析 \n",
    "```\n",
    "fit( x, y, batch_size=32, epochs=10, verbose=1, callbacks=None,\n",
    "validation_split=0.0, validation_data=None, shuffle=True, \n",
    "class_weight=None, sample_weight=None, initial_epoch=0)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "* x：输入数据。如果模型只有一个输入，那么x的类型是numpy array，如果模型有多个输入，那么x的类型应当为list，list的元素是对应于各个输入的numpy array  \n",
    "* y：标签，numpy array  \n",
    "* batch_size：整数，指定进行梯度下降时每个batch包含的样本数。训练时一个batch的样本会被计算一次梯度下降，使目标函数优化一步。  \n",
    "* epochs：整数，训练终止时的epoch值，训练将在达到该epoch值时停止，当没有设置initial_epoch时，它就是训练的总轮数，否则训练的总轮数为epochs - inital_epoch  \n",
    "* verbose：日志显示，0为不在标准输出流输出日志信息，1为输出进度条记录，2为每个epoch输出一行记录  \n",
    "* callbacks：list，其中的元素是keras.callbacks.Callback的对象。这个list中的回调函数将会在训练过程中的适当时机被调用，参考回调函数  \n",
    "* validation_split：0~1之间的浮点数，用来指定训练集的一定比例数据作为验证集。验证集将不参与训练，并在每个epoch结束后测试的模型的指标，如损失函数、精确度等。注意，validation_split的划分在shuffle之前，因此如果你的数据本身是有序的，需要先手工打乱再指定validation_split，否则可能会出现验证集样本不均匀。  \n",
    "* validation_data：形式为（X，y）的tuple，是指定的验证集。此参数将覆盖validation_spilt。\n",
    "* shuffle：布尔值或字符串，一般为布尔值，表示是否在训练过程中随机打乱输入样本的顺序。若为字符串“batch”，则是用来处理HDF5数据的特殊情况，它将在batch内部将数据打乱。  \n",
    "* class_weight：字典，将不同的类别映射为不同的权值，该参数用来在训练过程中调整损失函数（只能用于训练）  \n",
    "* sample_weight：权值的numpy array，用于在训练时调整损失函数（仅用于训练）。可以传递一个1D的与样本等长的向量用于对样本进行1对1的加权，或者在面对时序数据时，传递一个的形式为（samples，sequence_length）的矩阵来为每个时间步上的样本赋不同的权。这种情况下请确定在编译模型时添加了sample_weight_mode=’temporal’。Timestep-wise sample weighting (use of sample_weight_mode=\"temporal\") is restricted to outputs that are at least 3D, i.e. that have a time dimension.\n",
    "* initial_epoch: 从该参数指定的epoch开始训练，在继续之前的训练时有用。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def scheduler(epoch):\n",
    "# 每隔10个epoch，学习率减小为原来的1/10\n",
    "    if epoch % 10 == 0 and epoch != 0:\n",
    "        lr = Kbackend.get_value(model.optimizer.lr)\n",
    "        Kbackend.set_value(model.optimizer.lr, lr * 0.1)\n",
    "        print(\"lr changed to {}\".format(lr * 0.1))\n",
    "    return Kbackend.get_value(model.optimizer.lr)\n",
    "\n",
    "# keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)\n",
    "if learning_rate_deacy_policy == 1:\n",
    "    reduce_lr = [\n",
    "        LearningRateScheduler(scheduler),\n",
    "         # tf.keras.callbacks.ModelCheckpoint(model_file,\n",
    "          #                      save_best_only=True)\n",
    "    ]\n",
    "elif learning_rate_deacy_policy == 2:\n",
    "    reduce_lr = [\n",
    "        ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience = 5,verbose=2, mode='auto'),\n",
    "    ]\n",
    "        \n",
    "else:\n",
    "     # reduce_lr =  PrintDot()\n",
    "    reduce_lr = [\n",
    "        PrintDot(),\n",
    "    ]\n",
    "    \n",
    "# 原文链接：https://blog.csdn.net/zzc15806/article/details/79711114"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "my_callbacks = [\n",
    "    tf.keras.callbacks.TensorBoard(log_dir =  curve_reconstract_logs_child_dir),\n",
    "    tf.keras.callbacks.ModelCheckpoint(model_file,save_best_only=True),\n",
    "    tf.keras.callbacks.EarlyStopping(patience=5,mode='auto', min_delta=1e-9),\n",
    "]\n",
    "\n",
    "\n",
    "# tensorboard = TensorBoard(log_dir = my_log_dir + '\\{}'.format(log_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_stage: test\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"train\":\n",
    "    if (flag == 1) or (flag == 2):\n",
    "        print(\"flag:\",flag)\n",
    "        My_X = train_X\n",
    "        My_Y = train_Y\n",
    "        My_Test_X = test_X\n",
    "        My_Test_Y = test_Y\n",
    "        My_Weight =  train_weight #\n",
    "    else:\n",
    "        print(\"flag:\",flag)\n",
    "        My_X = trainX # trainX, dataX\n",
    "        My_Y = trainY # trainY， dataY \n",
    "        My_Test_X = testX\n",
    "        My_Test_Y = testY\n",
    "        My_Weight = train_weight  # train_weight ,weight_matrix\n",
    "        # history = model.fit(train_X, train_Y, batch_size=BATCH_SIZE,epochs=EPOCHS,\n",
    "        #                validation_split = 0.1, verbose=1,callbacks=[PrintDot()])\n",
    "    #     dataset = tf.data.Dataset.from_tensor_slices((train_X, train_Y))\n",
    "    #     train_dataset  = dataset.shuffle(len(train_Y)).batch(1)\n",
    "    #     t_dataset = tf.data.Dataset.from_tensor_slices((test_X,test_Y))\n",
    "    #     val_dataset  = dataset.shuffle(len(test_Y)).batch(1)\n",
    "    #     history = model.fit(train_dataset, epochs=EPOCHS,\n",
    "    #                      validation_data = val_dataset, verbose=1,callbacks=[PrintDot()])\n",
    "\n",
    "    if batch_size_strategy == True:\n",
    "        if save_logs == False:\n",
    "            history = model.fit(My_X, My_Y, batch_size = BATCH_SIZE,epochs=EPOCHS,validation_data = (My_Test_X,My_Test_Y),sample_weight = My_Weight, verbose=1,callbacks=reduce_lr)\n",
    "        else:\n",
    "            history = model.fit(My_X, My_Y, batch_size = BATCH_SIZE,epochs=EPOCHS,validation_data = (My_Test_X,My_Test_Y),sample_weight = My_Weight, verbose=1,callbacks=my_callbacks)\n",
    "    else:\n",
    "        if save_logs == False:\n",
    "            history = model.fit(My_X, My_Y, epochs = EPOCHS,validation_data = (My_Test_X,My_Test_Y),sample_weight = My_Weight, verbose=1,callbacks = reduce_lr)\n",
    "        else:\n",
    "            history = model.fit(My_X, My_Y, epochs = EPOCHS,validation_data = (My_Test_X,My_Test_Y),sample_weight = My_Weight, verbose=1,callbacks = my_callbacks)\n",
    "else:\n",
    "    print(\"model_stage:\", model_stage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Model testing(模型测试)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "model.evaluate输入数据(data)和金标准(label),然后将预测结果与金标准相比较,得到两者误差并输出.  \n",
    "model.predict输入数据(data),输出预测结果  \n",
    "* 是否需要真实标签(金标准)  \n",
    "model.evaluate需要,因为需要比较预测结果与真实标签的误差  \n",
    "model.predict不需要,只是单纯输出预测结果,全程不需要金标准的参与.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_history(history,model_training_img_file_saving_path,model_training_img_name):\n",
    "    hist = pd.DataFrame(history.history)\n",
    "    hist['epoch'] = history.epoch\n",
    "\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Abs Error [MAE]')\n",
    "    plt.plot(hist['epoch'], hist['mae'],label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mae'],label = 'Val Error')\n",
    "    # plt.ylim([0,5])\n",
    "    plt.legend()\n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name + '_MAE.png', dpi=96,  bbox_inches='tight')\n",
    "\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Square Error [MSE]')\n",
    "    plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "    # plt.ylim([0,20])\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "        learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS) + '_loss.png', dpi=96,  bbox_inches='tight')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    hist = pd.DataFrame(history.history)\n",
    "    hist['epoch'] = history.epoch\n",
    "    print(hist.tail())\n",
    "    plot_history(history,model_training_img_file_saving_path,model_training_img_name)\n",
    "    loss_csv_file_save = os.path.join(csv_file_saving_path ,   model_name + \"trainloss.csv\")\n",
    "    hist.to_csv(loss_csv_file_save, mode='w',float_format='%.4f',sep=',',index=None,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    plt.figure(figsize=(6,40))\n",
    "    if (flag == 1) or (flag == 2):\n",
    "        predictions = model.predict(test_X)\n",
    "#         np.testing.assert_allclose(predictions, test_Y, atol=1e-6)\n",
    "        loss, mae, mse = model.evaluate(test_X, test_Y, verbose=2)\n",
    "        sample_index = np.arange(len(test_Y))\n",
    "        plt.plot(test_Y,sample_index, label=\"实测\")  \n",
    "    else:\n",
    "        predictions = model.predict(testX)\n",
    "#         np.testing.assert_allclose(predictions, testY, atol=1e-6)\n",
    "        loss, mae, mse = model.evaluate(testX, testY, verbose=2)\n",
    "        sample_index = np.arange(len(testY))\n",
    "        plt.plot(testY,sample_index, label=\"实测\")\n",
    "    # plt.plot(test_Y_calc,sample_index, label=\"计算\")\n",
    "    plt.plot(predictions,sample_index, label=\"预测\")\n",
    "    plt.grid(True)#显示网格线\"\n",
    "    plt.xlabel( element_name)\n",
    "    plt.ylabel(\"验证样本编号\")\n",
    "    plt.title(element_name + \"在验证集上\")\n",
    "    plt.legend(loc='best')\n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "        learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_val.png', dpi=96,  bbox_inches='tight')\n",
    "    plt.show()\n",
    "    print(\"Testing set Mean Abs Error: {:5.5f} \".format(mae))\n",
    "    print(\"Testing set Mean Abs Error: {:5.5f} \".format(mse))\n",
    "# 对于测试阶段目前暂不计算MSE，等反归一化后计算  \n",
    "# else:\n",
    "#    A_Y_predict = model.predict(testALL_A_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Linear correlation analysis between the predicted value of the validation set and the true calibration value（验证集预测值与真实标定值线性相关性分析)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    if (flag == 1) or (flag == 2):\n",
    "        GT = test_Y\n",
    "    else:\n",
    "        GT = testY\n",
    "    \n",
    "    rmse = np.sqrt(mean_squared_error(GT, predictions))  \n",
    "    rmae = np.sqrt(mean_absolute_error(GT, predictions)) \n",
    "    ols = sm.OLS(GT, predictions).fit()\n",
    "    \n",
    "\n",
    "    print('验证集预测值与真实标定值线性相关性分析')\n",
    "    print(ols.summary())\n",
    "    print('Test RMSE: %.3f' % rmse)\n",
    "    print('Test RMAE: %.3f' % rmae)\n",
    "    rmse = \"{:.9f}\".format(rmse)\n",
    "    rmae = \"{:.9f}\".format(rmae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    summary_result = sm.iolib.summary.Summary.as_text(ols.summary())\n",
    "    corr_index = summary_result.split(\"\\nModel\")[0].split(\"R-squared (uncentered):\")[-1].strip()\n",
    "    print(corr_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    float(corr_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "error = []\n",
    "if model_stage == \"train\":\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.subplot(121)\n",
    "    if (flag == 1) or (flag == 2):\n",
    "        plt.scatter(test_Y, predictions)\n",
    "        error = predictions - test_Y\n",
    "        # rmse = np.sqrt(mean_squared_error(test_Y, predictions))\n",
    "    else:\n",
    "        plt.scatter(testY, predictions)\n",
    "        error = predictions - testY\n",
    "        # rmse = np.sqrt(mean_squared_error(testY, predictions))\n",
    "    plt.xlabel('True Values [MPG]')\n",
    "    plt.ylabel('Predictions [MPG]')\n",
    "    plt.axis('equal')\n",
    "    plt.axis('square')\n",
    "    plt.xlim([0,plt.xlim()[1]])\n",
    "    plt.ylim([0,plt.ylim()[1]])\n",
    "    plt.text(0.1 * plt.xlim()[1],0.9 * plt.ylim()[1], 'RMSE:' + str(rmse),fontdict=font)\n",
    "    plt.text(0.1 * plt.xlim()[1],0.85 * plt.ylim()[1], 'RMAE:' + str(rmae),fontdict=font)\n",
    "    plt.text(0.65 * plt.xlim()[1],0.1 * plt.ylim()[1], 'R:' + str(corr_index),fontdict=font)\n",
    "    _ = plt.plot([-100, 100], [-100, 100])\n",
    "    # plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "    #    learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_jiaohui.png', dpi=96,  bbox_inches='tight')\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    plt.hist(error, bins = 25)\n",
    "    plt.xlabel(\"Prediction Error [MPG]\")\n",
    "    _ = plt.ylabel(\"Count\")\n",
    "    \n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "        learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_predError_Distribution.png', dpi=96,  bbox_inches='tight')\n",
    "    plt.show()\n",
    "    # print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Model Save(模型保存)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# if model_stage == \"train\":\n",
    "#     if paly_music == True:\n",
    "#         mixer.init()\n",
    "#         music_path = \"music/\"\n",
    "#         file = \"MISIA - 星のように.mp3\"\n",
    "#         music_file = os.path.join(music_path,file)\n",
    "#         mixer.music.load(music_file)\n",
    "#         mixer.music.play()\n",
    "#         time.sleep(10)\n",
    "#         mixer.music.stop()\n",
    "        # exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing...\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"train\":\n",
    "    json_config = model.to_json()\n",
    "    with open(model_json, 'w') as json_file:\n",
    "        json_file.write(json_config)\n",
    "\n",
    "    model.save_weights(model_file)\n",
    "    print(\"Model Save is Finished!\")\n",
    "else:\n",
    "    print(\"Testing...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Post-processing of the prediction results(对预测结果后处理)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## The part that is predicted to be less than 0 is processed as 0（对预测小于0的部分进行取0处理）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21/21 [==============================] - 3s 3ms/step\n",
      "[0.07825512] (641, 1)\n"
     ]
    }
   ],
   "source": [
    "if model_stage == \"train\":\n",
    "    # A_Y_predict = predictions\n",
    "    A_Y_predict = model.predict(dataX)\n",
    "else:\n",
    "    A_Y_predict = model.predict(testALL_A_X)\n",
    "    print(min(A_Y_predict),A_Y_predict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "A_Y_predict_final_GY = A_Y_predict.copy()\n",
    "for j in range(0, len(A_Y_predict_final_GY)):\n",
    "    if A_Y_predict[j] < 0:\n",
    "        A_Y_predict_final_GY[j] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred_element_index: 0\n"
     ]
    }
   ],
   "source": [
    "# 查找element_name的索引\n",
    "pred_element_index = element.index(element_name)\n",
    "print(\"pred_element_index:\",pred_element_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, 300]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_log_name[pred_element_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Reverse normalization reduces the prediction curve（根据归一化范围还原对应分辨率的预测曲线）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "testALL_Y_predict_final = sen.revivification_scaler(A_Y_predict_final_GY,e_log_name,pred_element_index)\n",
    "if model_stage == \"train\":\n",
    "    true_Y = sen.revivification_scaler(dataY,e_log_name,pred_element_index)\n",
    "    Y_clac = sen.revivification_scaler(dataY_calc,e_log_name,pred_element_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\":\n",
    "    print(A_Y_predict_final_GY.shape,\"testALL_Y_predict_final.shape:\",testALL_Y_predict_final.shape,dataY_calc.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# testALL_Y_predict_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xbs\\AppData\\Local\\Temp\\ipykernel_11576\\3977957438.py:7: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
      "  plt.subplot(121)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,40))\n",
    "plt.title(element_name + \"预测曲线在训练验证集上\")\n",
    "e_max = max(testALL_Y_predict_final) * 1.2\n",
    "e_min = min(testALL_Y_predict_final) * 0.8\n",
    "plt.xlim(e_min,e_max)\n",
    "if use_depth_log == True:\n",
    "    plt.subplot(121)\n",
    "    plt.ylim(DEPTH_AddReslution[-1][0],DEPTH_AddReslution[0][0])\n",
    "    plt.plot(testALL_Y_predict_final,DEPTH_AddReslution,color=\"dodgerblue\", label= element_name + \"_预测还原\")\n",
    "    if model_stage == \"train\":\n",
    "        plt.plot(true_Y,DEPTH_AddReslution,color=\"dodgerblue\", label=\"实测\" + element_name)\n",
    "        plt.subplot(122)\n",
    "        plt.plot(testALL_Y_predict_final,DEPTH_AddReslution,color=\"green\", label= element_name + \"_预测还原\")\n",
    "        plt.plot(Y_clac,DEPTH_AddReslution,color=\"orange\", label=\"计算\" + element_name)\n",
    "    plt.ylabel(\"深度（m）\")\n",
    "    plt.xlabel(element_name)\n",
    "    plt.legend(loc='best')\n",
    "    plt.grid(True)\n",
    "else:\n",
    "    plt.subplot(121)\n",
    "    all_sample_index = np.arange(len(testALL_Y_predict_final))\n",
    "    plt.plot(testALL_Y_predict_final,all_sample_index,color=\"green\", label= element_name + \"_预测还原\")\n",
    "    plt.legend(loc='best')\n",
    "    plt.grid(True)\n",
    "    if model_stage == \"train\":\n",
    "        plt.plot(true_Y,all_sample_index,color=\"dodgerblue\", label=\"实测\" + element_name)\n",
    "        plt.legend(loc='best')\n",
    "        plt.subplot(122)\n",
    "        plt.plot(testALL_Y_predict_final,all_sample_index,color=\"green\", label= element_name + \"_预测还原\")\n",
    "        plt.plot(Y_clac,all_sample_index,color=\"orange\", label=\"计算\" + element_name)\n",
    "    plt.ylabel(\"样本编号\")\n",
    "    plt.xlabel(element_name) \n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "if model_stage == \"train\":\n",
    "    pred_image_save_path = model_training_img_file_saving_path \n",
    "    pred_image_name = model_training_img_name\n",
    "else:\n",
    "    pred_image_save_path = model_testing_img_file_saving_path \n",
    "    pred_image_name = model_testing_image_name + str(begin_depth) + \"-\"+ str(end_depth) + \"m\" \n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "plt.savefig(pred_image_save_path + pred_image_name + '_PredictionAll.png', dpi=96,  bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# The validation operation of the training phase ends with this program（训练阶段的验证操作到此程序结束）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "testdata_Y_predict_final = testALL_Y_predict_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Total Evaluation Module（总评价模块）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "add_flag = 0\n",
    "if model_stage == \"train\":\n",
    "    # 训练阶段\n",
    "    High_R_ALL = inputY.loc[:, [element_name]]\n",
    "    High_R = np.array(High_R_ALL)\n",
    "    High_R.shape = (len(High_R),)\n",
    "    print(\"真实标定值为训练数据标签！！！\")\n",
    "    add_flag = 1\n",
    "    print(add_flag)\n",
    "else:\n",
    "    if use_high_R_data == True:\n",
    "    # 测试阶段\n",
    "        High_R_ALL = pd.read_csv(HighRDataPath,engine='python',encoding='GBK')\n",
    "        High_R = High_R_ALL.loc[:, [element_name]]\n",
    "        High_R = np.array(High_R)\n",
    "        High_R.shape = (len(High_R),)\n",
    "        add_flag = 2\n",
    "        print(add_flag)\n",
    "    else:\n",
    "        print(\"无真实标定值！！！\")\n",
    "        add_flag = 3\n",
    "        print(add_flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Draw line and scatter charts（绘制折线图和散点图）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "绘制折线图，预测结果与真实标定趋势对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if model_stage == \"train\" and use_depth_log == True:\n",
    "    plt.figure(figsize=(6, 80))\n",
    "    plt.title(\"TrainALL_Y_predict_final_element_log_revivification\")\n",
    "# 说明曲线长度没有矫正过程，预测曲线为testALL_Y_predict_final == testdata_Y_predict_final\n",
    "    if (flag == 0) or (flag == 1) or (flag == 2):\n",
    "        High_R_Label = High_R.copy()\n",
    "        print(\"2\")\n",
    "        plt.plot(testdata_Y_predict_final,DEPTH_AddReslution,color=\"red\", label=\"predict_final\")\n",
    "        plt.plot(High_R_Label,DEPTH_AddReslution,color=\"black\", label=\"GroundTruth\")\n",
    "    else:\n",
    "        print(\"3\")\n",
    "        High_R_Label = sen.build_HighReslution_Label(High_R, seq_length)\n",
    "        plt.plot(testdata_Y_predict_final,DEPTH_AddReslution,color=\"red\", label=\"predict_final\")\n",
    "        plt.plot(High_R_Label,DEPTH_AddReslution,color=\"black\", label=\"GroundTruth\") \n",
    "           \n",
    "    plt.xlabel(element_name)\n",
    "    plt.ylabel(\"DEPTH(m)\")\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "        learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_constract_Real.png', dpi=96,  bbox_inches='tight')\n",
    "    plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_depth_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if (model_stage == \"train\" and use_depth_log == False) or (model_stage == \"test\" and use_high_R_data == True):\n",
    "    if (flag == 0) or (flag == 1):\n",
    "        High_R_Label = High_R.copy()\n",
    "    else:\n",
    "        High_R_Label = sen.build_HighReslution_Label(High_R, seq_length)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(641,)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot_data1 = pd.DataFrame(testdata_Y_predict_final,columns=[\"预测\" + element_name])\n",
    "# plot_data2 = pd.DataFrame(Y_clac,columns=[\"计算\" + element_name])\n",
    "# plot_data = pd.concat([plot_data1,plot_data2],axis=1)\n",
    "# plot_data\n",
    "testdata_Y_predict_final.shape = (len(testdata_Y_predict_final),)\n",
    "testdata_Y_predict_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(641,)\n"
     ]
    }
   ],
   "source": [
    "if use_high_R_data == True:\n",
    "     print(High_R_Label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "if model_stage == \"train\" or ((model_stage == \"test\") and (add_flag == 2)):\n",
    "    value_max = max(High_R_Label) * 1.05\n",
    "    value_min = -0.2 + min(High_R_Label) \n",
    "    # 说明没有低分辨率曲线，那么就是训练阶段或是有高分辨率的\n",
    "    plt.figure(figsize=(8, 24))\n",
    "    plt.subplot(311)\n",
    "    plt.title(\"预测值和真实值比较\")\n",
    "    plt.scatter(testdata_Y_predict_final,High_R_Label,color=\"dodgerblue\")\n",
    "    plt.grid(linestyle = '--')\n",
    "    plt.xlabel(\"预测\" + element_name)\n",
    "    plt.ylabel(\"真实值\")\n",
    "    if model_stage == \"train\":\n",
    "        plt.subplot(312)\n",
    "        plt.title(\"预测值和计算值比较\")\n",
    "        plt.scatter(testdata_Y_predict_final,Y_clac,color=\"orange\")\n",
    "        plt.grid(linestyle = '--')\n",
    "        plt.xlabel(\"预测\" + element_name)\n",
    "        plt.ylabel(\"计算\" + element_name)\n",
    "        plt.subplot(313)\n",
    "        plt.title(\"预测值和计算值比较\")\n",
    "        plt.scatter(true_Y,Y_clac,color=\"green\")\n",
    "        plt.grid(linestyle = '--')\n",
    "        plt.xlabel(\"真实\" + element_name)\n",
    "        plt.ylabel(\"计算\" + element_name)\n",
    "    plt.xlim(value_min,value_max)\n",
    "    plt.ylim(value_min,value_max)\n",
    "    plt.tight_layout()\n",
    "    if model_stage == \"train\":    \n",
    "        plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "            learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_constract_scatterMap.png', dpi=96,  bbox_inches='tight')\n",
    "    else:\n",
    "        plt.savefig(model_testing_img_file_saving_path + model_testing_image_name + '_constract_scatterMap.png', dpi=96,  bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if model_stage == \"test\" and use_depth_log == True:\n",
    "    \n",
    "    plt.figure(figsize=(10, 12))\n",
    "    plt.title(\"testALL_Y_predict_final_element_log_revivification\")\n",
    "    # 说明曲线长度没有矫正过程，预测曲线为testALL_Y_predict_final == testdata_Y_predict_final\n",
    "    if (flag == 0) or (flag == 1):\n",
    "        print(\"2\")\n",
    "        if add_flag == 2:\n",
    "            High_R_Label = High_R\n",
    "        plt.plot(testdata_Y_predict_final,DEPTH_AddReslution,color=\"red\", label=\"predict_final\")\n",
    "        plt.plot(High_R_Label,DEPTH_AddReslution,color=\"orange\", label=\"GroundTruth\") \n",
    "    else:\n",
    "        print(\"3\")\n",
    "        if add_flag == 2:\n",
    "            High_R_Label = High_R\n",
    "            High_R_Label = sen.build_HighReslution_Label(High_R, seq_length)\n",
    "            plt.plot(High_R_Label,DEPTH_AddReslution,color=\"orange\", label=\"GroundTruth\")\n",
    "        plt.plot(testdata_Y_predict_final,DEPTH_AddReslution,color=\"red\", label=\"predict_final\")\n",
    "             \n",
    "            \n",
    "    plt.xlabel(element_name + \"(%)\")\n",
    "    plt.ylabel(\"DEPTH(m)\")\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_cols = 2\n",
    "fig_rows = 2\n",
    "plt.figure(figsize=(4 * fig_cols, 4 * fig_rows))\n",
    "if (use_high_R_data == True) or (model_stage == \"train\"):\n",
    "    a = np.histogram(High_R_Label)[0]\n",
    "    b = np.histogram(High_R_Label)[1]    \n",
    "else:    \n",
    "    a = np.histogram(testdata_Y_predict_final)[0]\n",
    "    b = np.histogram(testdata_Y_predict_final)[1]\n",
    "\n",
    "ax1 = plt.subplot2grid((fig_rows, fig_cols), (0, 0))\n",
    "ax1.hist(testdata_Y_predict_final,color = \"dodgerblue\", label = \"预测\" + element_name, bins = b[1:len(b)] )\n",
    "ax1.legend(loc=\"upper right\")\n",
    "ax1.grid(linestyle = '--')\n",
    "    \n",
    "    \n",
    "if model_stage == \"train\":\n",
    "    ax2 = plt.subplot2grid((fig_rows, fig_cols), (0, 1))\n",
    "    ax2.hist(High_R_Label,color = \"black\",label = \"真实值\",bins = b[1:len(b)])\n",
    "    ax2.legend(loc=\"upper right\")\n",
    "    ax2.grid(linestyle = '--') \n",
    "elif (model_stage == \"test\") and (add_flag == 2):\n",
    "    ax2 = plt.subplot2grid((fig_rows, fig_cols), (0, 1))\n",
    "    ax2.hist(High_R_Label,color = \"black\",label = \"真实值\",bins = b[1:len(b)])\n",
    "    ax2.legend(loc=\"upper right\")\n",
    "    ax2.grid(linestyle = '--')\n",
    "    \n",
    "\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.grid(linestyle = '--')\n",
    "if model_stage == \"train\":\n",
    "    plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "    learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '-hist.png', dpi=96,  bbox_inches='tight')\n",
    "else:\n",
    "    plt.savefig(model_testing_img_file_saving_path + model_testing_image_name + '-hist.png', dpi=96,  bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Linear correlation analysis between predicted and true calibration values(预测值与真实标定值线性相关性分析)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值与真实标定值线性相关性分析\n",
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   0.998\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.998\n",
      "Method:                 Least Squares   F-statistic:                          3.024e+05\n",
      "Date:                Fri, 21 Apr 2023   Prob (F-statistic):                        0.00\n",
      "Time:                        17:01:24   Log-Likelihood:                         -2105.0\n",
      "No. Observations:                 641   AIC:                                      4212.\n",
      "Df Residuals:                     640   BIC:                                      4216.\n",
      "Df Model:                           1                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             0.9941      0.002    549.879      0.000       0.991       0.998\n",
      "==============================================================================\n",
      "Omnibus:                       51.241   Durbin-Watson:                   0.085\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               70.376\n",
      "Skew:                          -0.626   Prob(JB):                     5.23e-16\n",
      "Kurtosis:                       4.034   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Test RMSE: 6.509\n",
      "Test RMAE: 2.232\n"
     ]
    }
   ],
   "source": [
    "if (model_stage == \"train\") or ((model_stage == \"test\") and (add_flag == 2)):\n",
    "    rmse = np.sqrt(mean_squared_error(High_R_Label, testdata_Y_predict_final))\n",
    "    rmae = np.sqrt(mean_absolute_error(High_R_Label, testdata_Y_predict_final)) \n",
    "    ols0 = sm.OLS(High_R_Label, testdata_Y_predict_final).fit()\n",
    "    print('预测值与真实标定值线性相关性分析')\n",
    "    print(ols0.summary())\n",
    "    print('Test RMSE: %.3f' % rmse)\n",
    "    print('Test RMAE: %.3f' % rmae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# if use_high_R_data == True:\n",
    "#     if use_low_R_data == True:\n",
    "#         MAE = testdata_Y_predict_final - High_R_Label\n",
    "#     else:\n",
    "#         MAE = testALL_Y_predict_final - High_R_Label"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## View the distribution of prediction curves and true calibrations(查看预测曲线和真实标定的分布)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "font = {'family':'SimHei',\n",
    "     'style':'italic',\n",
    "    'weight':'normal',\n",
    "      'color':'red',\n",
    "      'size':16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Program Files\\Python310\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "d:\\Program Files\\Python310\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if use_high_R_data == True:\n",
    "if (model_stage == \"train\") or ((model_stage == \"test\") and (add_flag == 2)):\n",
    "    sns.set_style('white')\n",
    "    sns.set(font=font['family'])\n",
    "        # 图表风格设置\n",
    "        # 风格选择包括：\"white\", \"dark\", \"whitegrid\", \"darkgrid\", \"ticks\"\n",
    "    \n",
    "    plt.figure(figsize=(12, 4))#绘制画布\n",
    "    plt.title(\"Pred and AddR Distribution\")  # 添加图表名\n",
    "    sns.distplot(testdata_Y_predict_final,  kde=True,label = \"预测_\" + element_name,hist=True,color='r')\n",
    "    sns.distplot(High_R_Label,  label = \"真实值\", kde=True,hist=True,color='b')\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.grid(linestyle = '--')     # 添加网格线\n",
    "   \n",
    "\n",
    "    if model_stage == \"train\":\n",
    "        plt.savefig(model_training_img_file_saving_path + model_training_img_name +\"_\" + element_name + '_'+ str(n_layers) + \"_layers_\" +  \"_lr_\" + str(\n",
    "            learning_rate) + \"h_dim\" + str(hidden_dim) + \"_epoch_\" + str(EPOCHS)+ '_add-R_hist.png', dpi=96,  bbox_inches='tight')\n",
    "    else:\n",
    "        plt.savefig(model_testing_img_file_saving_path + model_testing_image_name + '_add-R-hist.png', dpi=96,  bbox_inches='tight')\n",
    "\n",
    "    plt.show()\n",
    "elif ((model_stage == \"test\") and (add_flag == 3)):\n",
    "    \n",
    "    sns.set_style('white')\n",
    "        # 图表风格设置\n",
    "        # 风格选择包括：\"white\", \"dark\", \"whitegrid\", \"darkgrid\", \"ticks\"\n",
    "\n",
    "    plt.figure(figsize=(12, 4))#绘制画布\n",
    "    sns.distplot(testALL_Y_predict_final,  kde=True,label = \"预测_\" + element_name,hist=False,color='r')\n",
    "        # sns.distplot(C_element,  label = \"C_element\", kde=True,hist=False,color='b')\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.grid(linestyle = '--')     # 添加网格线\n",
    "    plt.title(\"Pred and AddR Distribution\")  # 添加图表名\n",
    "    plt.savefig(model_testing_img_file_saving_path + model_testing_image_name + '_add-R-D.png', dpi=96,  bbox_inches='tight')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Save the curve results(曲线结果保存)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Result file header information(结果文件文件头信息)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'松页油1'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filename_A ： 'HP1_orginLog_6D_4075m-4280m_R_0.125.csv'\n",
    "well_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin_depth,end_depth: 2449.0 2529.0\n"
     ]
    }
   ],
   "source": [
    "if use_depth_log == True:\n",
    "    print(\"begin_depth,end_depth:\",begin_depth,end_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.125"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolution_ratio = 1 / resolution\n",
    "resolution_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## View the saved result curves（查看保存的结果曲线）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if use_depth_log == False:\n",
    "    depth_log = all_sample_index\n",
    "    DEPTH_AddReslution = all_sample_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Algorithm is Finished!!\n"
     ]
    }
   ],
   "source": [
    "if use_depth_log == True:\n",
    "    pd_data0 = pd.DataFrame(DEPTH_AddReslution,columns=[\"DEPTH\"])\n",
    "    result_csv_name = well_name + \"_\" + layer_name + \"_\"+ str(begin_depth) + \"_\"+ str(end_depth) + \"m_\" + element_name + '_Pred_R_'+ sen.tid_maker() +'.txt'\n",
    "else:\n",
    "    pd_data0 = pd.DataFrame(all_sample_index,columns=[\"sample_index\"])\n",
    "    result_csv_name = well_name + \"_\" + layer_name + \"_\" + element_name + '_Pred_R_'+ sen.tid_maker() +'.txt'\n",
    "pd_data1 = pd.DataFrame(testALL_Y_predict_final,columns=[element_name + \"_pred\"])\n",
    "if use_high_R_data:\n",
    "    pd_data2 = pd.DataFrame(High_R_Label,columns=[element_name + \"_High_R\"])\n",
    "    pd_data = pd.concat([pd_data0,pd_data1,pd_data2],axis=1)\n",
    "else:\n",
    "    pd_data = pd.concat([pd_data0,pd_data1],axis=1)\n",
    "pd_data.to_csv(csv_file_saving_path + result_csv_name,mode='w',float_format='%.4f',sep='\\t',index=None,header=True)\n",
    "print(\"Prediction Algorithm is Finished!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# if paly_music == True:\n",
    "#     mixer.init()\n",
    "#     music_path = \"music/\"\n",
    "#     file = \"MISIA - 星のように.mp3\"\n",
    "#     music_file = os.path.join(music_path,file)\n",
    "#     mixer.music.load(music_file)\n",
    "#     mixer.music.play()\n",
    "#     time.sleep(6)\n",
    "#     mixer.music.stop()\n",
    "#         # exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
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